With the development of satellite and remote-sensing techniques, countries around the world have been launching more and more sensor satellites, such as IKONOS, QuickBird, ZiYuan-3 and etc., which simultaneously acquired panchromatic and multispectral imagery. Since an imaging device makes a balance between spectral resolution and spatial resolution, the spatial resolution of panchromatic image is better than those of multispectral and hyperspectral images, while multispectral and hyperspectral images contain a plurality of spectral wave bands and their spectral resolutions are better than that of a panchromatic image. Thus, generation of fused image with sharpened spatial details and enriched spectral information, by sharpening multispectral and hyperspectral images with spatial detail information of panchromatic image, helps realizing better and more accurate extraction of needed information as well as fast and accurate analysis of images by image-interpreting personnel.
In the present application, multispectral images and hyperspectral images are collectively referred to as “spectral images”. Typically, spectral imagery consists of more than one wave bands, so a spectral image contains a plurality of components, each of which is a grey scale image. In practical use, a panchromatic and spectral image fusion method must satisfy the requirements of: spectral fidelity that the spectral information of the fused images needs to be kept consistent with the spectral information of the spectral image; detail fidelity that the information on spatial details needs to be kept consistent with that of the panchromatic image; and, high timeliness that the computation complexity of the fusion method needs to be low so as to realize fusion of panchromatic and spectral images of large sizes and big data amounts at high speed.
To date, lots of image fusion methods have been proposed in the art, such as fusion methods based on component-replacement, fusion methods based on IHS transform, PCA transform, Gram-Schmidt (GS) transform and so on, and fusion methods based on frequency decomposition including wavelet transform, curvelet transform etc. Generally, fusion methods based on component-replacement have good spatial detail fidelity, but the spectrum of their fused images has severe distortion in areas where great difference in luminance exists between panchromatic image and the replaced components. Fusion methods based on frequency decomposition have better spectral fidelity, but they have the defect of detail distortion. In addition, the above-mentioned fusion methods require complicated computation, and their computations for fusion process of large-size panchromatic and spectral images are extremely time-consuming. For example, with each of the two methods of the best application results so far, the fusion method based on GS transform provided by ENVI (The Environment for Visualizing Images) remote-sensing image processing software (abbreviated as ENVI-GS transform method) and panchromatic sharpening method provided by PCI (PCI Geomatica) remote-sensing image processing software (abbreviated as PCI sharpening method), the time for fusion computation is greater than 150 seconds for a panchromatic image of 12,000×12,000 pixels and a multispectral image of 3,000×3,000 pixels with a computer with a 3.2 GHz 4-core CPU, 2 GB of memory and Windows XP operating system, which can hardly satisfy the needs for high-efficiency.
In such a context, it is of great significance to develop a panchromatic sharpening fusion method capable of effectively avoiding spectral and spatial detail distortions of fused images and having low computation complexity, for fast and efficient generation of panchromatic and spectral fused image with high resolution.